BLOGS

Buried in the Economist‘s excellent special report “Biology 2.0” on the decade since the completion of the Human Genome Project is a chart that I almost didn’t believe when I saw it. Using data from M.I.T.’s Broad Institute, the cost of genomic sequencing (in dollars per million base pairs) was plotted against the cost of computing. The results were astonishing.

Moore’s Law (the speed of computing per dollar doubles every 18 months), perhaps the representative concept of ever accelerating technological progress and the foundation of Ray Kurzweil’s prediction of a technological singularity, looks pretty pathetic. And genomics is still getting cheaper and faster:

The genome sequenced by the International Human Genome Sequencing Consortium (actually a composite from several individuals) took 13 years and cost $3 billion. Now, using the latest sequencers from Illumina, of San Diego, California, a human genome can be read in eight days at a cost of about $10,000. Nor is that the end of the story. Another Californian firm, Pacific Biosciences, of Menlo Park, has a technology that can read genomes from single DNA molecules. It thinks that in three years’ time this will be able to map a human genome in 15 minutes for less than $1,000. And a rival technology being developed in Britain by Oxford Nanopore Technologies aspires to similar speeds and cost.

In terms of reductions in cost and speed, genomic sequencing has accomplished in a decade what took computing has been lurching towards for over half a century. Imagine going from the ENIAC to the iPad in fifteen years instead of sixty—that’s what genomic sequencing did. It’s hard to even imagine what will happen when a person’s genome can be sequenced in under an hour for the cost of a new computer. On its own, this news is amazing. Genomic sequencing’s progress is also frustrating, however, given the near non-existence of drugs and treatments based in genomic research. Only now, with the ability to sequence multiple genomes at a reasonable (if still rather steep) price, are researchers beginning to gain traction. While it’s well and good to be able to sequence a genome, we’re still a ways off from doing anything with the information.

The steep fall of costs and time for genomic sequencing mixed with the significant lack of real, medical applications like drugs or diagnostic tools has troubling implications for those committed to the futuristic prophesies of a technological Singularity. The technological Singularity, pushed by Kurzweil and his zealots at the Singularity University (recently profiled in the New York Times and rightly ridiculed at Scientific American) is based in an extrapolation of Moore’s Law to all of human technological progress. Kurzweil believes that, based on his calculations, technological change is accelerating at such a pace that by 2045 progress will become incomprehensibly rapid. Things like genuine artificial intelligence, self-replicating nanorobots, and human-machine hybrids will be effortless and prolific. Yet, in less than a decade, genomics has shown that improvements in the cost and speed of a technology do not guarantee real-world applications or immediate paradigm shifts in how we live our day-to-day lives.

Moore’s Law is moot and genetics (in terms of applications) is just beginning to boom. Back to the drawing boards, futurists: acceleration alone isn’t enough to build the future.

I think this is a really poor comparison to make. Reading the genetic code is a decoding process, while designing computer chips is an encoding process. DNA will always be the same, but computer chips have to continually get denser and denser.

A better comparison might be between astronomical observational abilities and reading the genome. I don’t know what metric would be useful for astronomy, so I don’t know what the comparison would look like.

Acceleration of hardware capability is not sufficient to build the future, but it is necessary, and in many cases, it is the most significant bottleneck. It’s painfully true that our knowledge of the genetic “compiler” (how the genotype translates to phenotypes, especially for complex phenomena like intelligence) is primitive, but as you said, our ability to analyze many examples of the “source code” (the genotype itself) very quickly means that “researchers [are] beginning to gain traction.” This traction was not possible beforehand.

Just like the invention of the microscope served no intrinsic purpose without scientists looking through the lenses, so too is the mere ability to rapidly sequence genomes useless. Microbiology did not come into existence fully-formed on the day someone first cobbled some pieces of bent glass together. However, that hardware capability enabled the field’s existence, and that branch of science arguably created a miniature technological singularity as a result, using Wikipedia’s definition of “[that which can] make the future … unpredictable and qualitatively different from today.” Living in a world moments after the mere inception of germ theory, as opposed to angry spirits who need to be appeased in order to keep one healthy, is a qualitative difference, don’t you think?

A world in which we can rapidly sequence genomes is, in its own small way, qualitatively different from a world in which we cannot, and between the many thousands of advances in other fields just like this one (which are bootstrapped by one another, like the advances in genomic analysis that will arise because of the demand created by this rapid sequencing technology), we will see the emergence of overall exponential growth and completely unpredictable progress that futurists like Kurzweil predict. Assaulting the idea because the timeline of progress looks exactly as predicted – a slow start for a long time, then explosive growth – is foolish.

The main reason we aren’t doing anything with all the information provided by genetic sequencing is because we understand little as to how the genes express themselves. This problem will be solved by an advance in computational ability. Just because genomics is ahead of the curve in terms of growth and availability does not discredit any predictions made. It simply means we need to wait until computational power catches up and allows us to make sense of all the data we have gathered from our genomes.

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